knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)

library(tidyverse)
library(here)
library(sf)
library(tmap)
### install.packages("tmap")
### update.packages(ask = FALSE)

Reading in the data

sf_trees <- read_csv(here("data", "sf_trees", "sf_trees.csv"))

Part 1: Wrangling and GGplot Review

Example 1: Find counts of observations by legal_status & wrangle a bit

### method 1: group_by() %>%  summarize()
sf_trees %>% 
  group_by(legal_status) %>% 
  summarize(tree_count = n())
## # A tibble: 10 × 2
##    legal_status                 tree_count
##    <chr>                             <int>
##  1 DPW Maintained                   141725
##  2 Landmark tree                        42
##  3 Permitted Site                    39732
##  4 Planning Code 138.1 required        971
##  5 Private                             163
##  6 Property Tree                       316
##  7 Section 143                         230
##  8 Significant Tree                   1648
##  9 Undocumented                       8106
## 10 <NA>                                 54
### method 2: different way plus a few new functions
top_5_status <- sf_trees %>% 
  count(legal_status) %>% 
  drop_na(legal_status) %>% 
  rename(tree_count = n) %>% 
  relocate(tree_count) %>% # easily re-order columns, this will relocate to front of my data
  slice_max(tree_count, n = 5) %>% 
  arrange(-tree_count) #- means arrange by descending, no - arrange by ascending or (desc(tree_count))

Make a graph of the top 5 from above

ggplot(data = top_5_status, aes(x = fct_reorder(legal_status, tree_count), y = tree_count)) +
  geom_col(fill = "darkgreen") +
  labs(x = "Legal Status", y = "Tree Count") +
  coord_flip() +
  theme_minimal()

Example 2: Only keep observations where legal status is “Permitted Site” and caretaker is “MTA”, and store as permitted_data_df

shift-control-c to comment/uncoment quickly (active or inactive code)

# sf_trees$legal_status %>% unique()
# unique(sf_trees$caretaker)
permitted_data_df <- sf_trees %>% 
  filter(legal_status == c("Permitted Site") & caretaker == "MTA")
# filter(legal_status %in% c("Permitted Site", "Private")

Example 3: Only keep Blackwood Acacia trees, and then only keep columns legal_status, date, latitude, longitude and store as blackwood_acacia_df

blackwood_acacia_df <- sf_trees %>% 
  filter(str_detect(species, "Blackwood Acacia")) %>% 
  select(legal_status, date, lat = latitude, lon = longitude)

### Make a little graph of locations
ggplot(data = blackwood_acacia_df, aes(x = lon, y = lat)) +
  geom_point(color = "darkgreen")

Example 4: Use tidyr::seperate() take on column and seperate it out

sf_trees_sep <- sf_trees %>% 
  separate(species, into = c('spp_scientific', 'spp_common'), sep = ' :: ')

Example 5: use ‘tidyr::unite()’

ex_5 <- sf_trees %>% 
  unite('id_status', tree_id, legal_status, sep = '_COOL_')

Part 2: make some maps

Step 1: convert lat/lon to spatial point, st_as_sf()

blackwood_accacia_sf <- blackwood_acacia_df %>% 
  drop_na(lat, lon) %>% 
  st_as_sf(coords = c('lon', 'lat'))

### we need to tell R what the coordinate refernce system is
st_crs(blackwood_accacia_sf) <- 4326

ggplot(data = blackwood_accacia_sf) +
  geom_sf(color = 'darkgreen')+
  theme_minimal()

Read in the SF shapefile and add to map

sf_map <- read_sf(here('data', 'sf_map', 'tl_2017_06075_roads.shp'))

sf_map_transform <- st_transform(sf_map, 4326)

ggplot(data = sf_map_transform) +
  geom_sf()

Combine the maps!

ggplot() +
  geom_sf(data = sf_map,
          size = .1,
          color = 'darkgrey') +
  geom_sf(data = blackwood_accacia_sf,
          color = 'red',
          size = 0.5) +
  theme_void() +
  labs(title = 'Blackwood acacias in SF')

Now an interactive map~

tmap_mode('view')

tm_shape(blackwood_accacia_sf) +
  tm_dots()